Classification of deviations in a process

ABSTRACT

A process analysis system includes sensors and a processing system. The sensors monitor the process to generate sensor signals. The processing system processes the sensor signals to detect a deviation from a baseline for the process. The processing system generates a process vector for the deviation in response to detecting the deviation. The processing system compares the process vector to a plurality of library vectors to classify the deviation. In some examples, the process comprises a system that supplies water.

RELATED CASES

[0001] This patent claims the benefit of U.S. provisional patentapplication 60/438,358, entitled “Classification of Deviations in aProcess”, filed on Jan. 7, 2003, and which is hereby incorporated byreference into this patent.

BACKGROUND OF THE INVENTION

[0002] 1. Field of the Invention

[0003] The invention is related to the field of process analysis, and inparticular, to technology that classifies deviations in the process.

[0004] 2. Statement of the Problem

[0005] Sensors measure the characteristics of a process. Such processesinclude water supply systems, manufacturing operations, electricaldistribution systems, and communication systems. For example, watersupply systems use sensors to measure pressure, temperature, and flowrate. Unfortunately, sensors have not been developed to detect everyabnormal condition. There may not be available sensors to detect if aspecific component fails, or to detect a specific contaminant in theprocess. Typically, it is too complex and expensive to develop a newsensor for each new abnormal condition of interest.

SUMMARY OF THE SOLUTION

[0006] Some examples of the invention include process analysis systemsand their methods of operation. The process analysis systems include aplurality of sensors and a processing system. The sensors are configuredto monitor the process to generate sensor signals. The processing systemis configured to process the sensor signals to detect a deviation from abaseline for the process, generate a process vector for the deviation inresponse to detecting the deviation, and compare the process vector to aplurality of library vectors to classify the deviation.

[0007] In some examples of the invention, the process comprises a systemthat supplies water.

[0008] In some examples of the invention, the sensor signals indicatepH, conductivity, turbidity, chlorine, and total organic carbon of thewater.

[0009] In some examples of the invention, the classified deviationcomprises a contaminant in the water.

[0010] In some examples of the invention, the processing system isconfigured to signal a control system to operate a valve in response toclassifying the deviation as a contaminant in the water.

[0011] In some examples of the invention, the processing system isconfigured to signal a control system to add a marker to the water inresponse to classifying the deviation as a contaminant in the water.

[0012] In some examples of the invention, the marker comprises acolorant.

[0013] In some examples of the invention, the processing system isconfigured to signal a control system to perform a treatment on thewater in response to classifying the deviation as a contaminant in thewater.

[0014] In some examples of the invention, the treatment comprises addinga disinfectant to the water.

[0015] In some examples of the invention, the treatment comprises addingchlorine to the water.

[0016] In some examples of the invention, the treatment comprisesexposing the water to ultraviolet radiation.

[0017] In some examples of the invention, the processing system isconfigured to process the sensor signals to produce a single variableand compare the single variable to a threshold to detect the deviationfrom the baseline.

[0018] In some examples of the invention, the process vector comprises aunit vector.

[0019] In some examples of the invention, the processing system isconfigured to compare an angle between the process vector and one of thelibrary vectors to a threshold.

[0020] In some examples of the invention, the library vectors areassociated with abnormal operations and the processing system isconfigured to identify one of the abnormal operations that is associatedwith one of the library vectors that matches the process vector toclassify the deviation.

[0021] In some examples of the invention, the processing system isconfigured to store the process vector as a new one of the libraryvectors and associate an abnormal operation with the new library vectorin response to an unknown classification.

DESCRIPTION OF THE DRAWINGS

[0022] The same reference number represents the same element on alldrawings.

[0023]FIG. 1 illustrates a process analysis system in an example of theinvention.

[0024]FIG. 2 illustrates a processing system in an example of theinvention.

[0025]FIG. 3 illustrates a signal processing and trigger detectionsystem in an example of the invention.

[0026]FIG. 4 illustrates a classification system in an example of theinvention.

[0027]FIG. 5 illustrates a water supply analysis system in an example ofthe invention.

DETAILED DESCRIPTION OF THE INVENTION

[0028] FIGS. 1-5 and the following description depict specific examplesto teach those skilled in the art how to make and use the best mode ofthe invention. For the purpose of teaching inventive principles, someconventional aspects have been simplified or omitted. Those skilled inthe art will appreciate variations from these examples that fall withinthe scope of the invention. Those skilled in the art will appreciatethat the features described below can be combined in various ways toform multiple variations of the invention. As a result, the invention isnot limited to the specific examples described below, but only by theclaims and their equivalents.

EXAMPLE #1

[0029]FIG. 1 illustrates process analysis system 100 in an example ofthe invention. Process analysis system 100 includes process 101, sensors102-104, processing system 105, and control systems 106-108. The numberof sensors and control systems shown on FIG. 1 has been restricted forclarity.

[0030] Process 101 is a dynamic operation that is characterized bymultiple parameters that can be measured by sensors 102-104. Process 101could be a water supply system, manufacturing operation, electricaldistribution system, communication system, or some other process that issuitable for the analysis of system 100.

[0031] Sensors 102-104 measure characteristics of process 101 andtransfer sensor signals 111 to processing system 105. Sensor signals 111indicate the measured characteristics, and thus, provide informationabout the current state of process 101. For a water supply system,sensors 102-104 may measure pH, conductivity, turbidity, chlorine, totalorganic carbon and other characteristics of the water flowing through awater main.

[0032] Control systems 106-108 exert control over process 101 inresponse to control signals 112 from processing system 105. For a watersupply system, control systems 106-108 may operate valves on the watermain, add a selected marker to the water in the water main, or performsome other treatment. The marker could include a colorant to markcontaminated water. The treatment could counteract contaminants throughthe addition of a disinfectant to the water, the exposure of the waterto ultraviolet radiation, or some other treatment.

[0033] Processing system 105 processes sensor signals 111 to identify adeviation from a baseline for process 101. In response to identifyingthe deviation, processing system 105 generates a process vector for thedeviation and compares the process vector to a plurality of libraryvectors to classify the deviation. Processing system 105 may providecontrol signals 112 to control systems 106-108 based on the classifieddeviation. Processing system 105 may also provide analysis data 113 toprocess operations.

[0034] For the water supply system, processing system 105 may identifyand classify a deviation indicating water contamination, and inresponse, instruct control systems 106-108 to operate valves that willshut off or divert the supply of contaminated water. In addition,processing system 105 may provide a contamination alarm to water supplyoperations.

[0035] Processing system 105 could include a computer, microprocessor,digital signal processor, application specific integrated circuitry,special purpose circuitry, or some other processing apparatus.Processing system 105 may execute instructions that direct processingsystem 105 to operate as described herein. The instructions couldinclude software, firmware, programmed integrated circuitry, or someother form of machine-readable instructions. Thus, a product may becomprised of a memory that stores the instructions. The memory could becomprised of a disk, tape, integrated circuit server, or some othermemory device. Processing system 105 could be a single device or a setof interoperating devices. Processing system 105 could be located at asingle site or distributed over a wide geographic area.

EXAMPLE #2

[0036]FIG. 2 illustrates processing system 200 in an example of theinvention. Processing system 200 is an example of processing system 100of FIG. 1. Processing system 200 includes signal processing system 201,trigger detection system 202, classification system 203, library system204, and control system 205.

[0037] Signal processing system 201 receives sensor signals 211 from thesensors that measure process characteristics. Signal processing system201 processes sensor signals 211 to provide processed sensor data 212 totrigger detection system 202 and classification system 203. The signalprocessing could entail reformatting the sensor signals, removing noise,replacing missing or false data, or other operations that prepare sensordata 212 for systems 202-203. Replacement data could be the last knowngood value, an average value, or a user-selected default value. If datais replaced a given number of times within a specific time period, thenan appropriate alarm could be generated.

[0038] Trigger detection system 202 receives processed sensor data 212.Trigger detection system 202 processes sensor data 212 to determine ifthere is a deviation from a baseline for the process. In response todetecting a deviation from the baseline, trigger detection system 202provides trigger indication 213 to classification system 203. To detectthe deviation from baseline, trigger detection system 202 may processsensor data 212 to produce a single logical variable whose stateindicates whether the process is operating normally or abnormally. Whenthe process is operating abnormally, the single logical variable shouldcause a trigger. One example of a single logical variable is the anglebetween two unit vectors, where one unit vector is derived from sensordata 212 and the other unit vector from a vector library and isassociated with an abnormal operation. Trigger detection techniquesinclude sensor data thresholds, genetic algorithms, pattern recognition,neural networks, process modeling, statistical methods, or anothersuitable approach to trigger detection.

[0039] Classification system 203 receives processed sensor data 212 andtrigger indication 213. In response to trigger indication 213,classification system 203 processes sensor data 212 to classify thedeviation. To classify the deviation, classification system 203processes sensor data 212 to generate a process vector. The processvector represents the current state of the process -where the processmay be operating abnormally as indicated by the trigger.

[0040] Classification system 203 also retrieves vector library 214 fromlibrary system 204. Vector library 214 includes a set of stored libraryvectors and their associated abnormal operations. Examples of abnormaloperations include component failure, contamination, operator error,unauthorized intrusion, or some other unwanted event that negativelyaffects the process. The stored library vectors are previously derivedthrough the observation of actual abnormal operations or through teststhat emulate various abnormal operations in order to associate aspecific abnormal operation with a specific library vector. Theseobservations and tests should use the same type of sensors andprocessing that produce the process vector.

[0041] Classification system 203 compares the process vector to thelibrary vectors to find a match. The match does not have to be absolute,and typically, two vectors are deemed a match if they are within aspecified range or angle of one another. Various techniques can be usedto determine a match between two vectors, including a minimum angleanalysis, a minimum distance calculation, nearest neighbor analysis, orsome other vector comparison technique. If the process vector matchesone of the library vectors, then the deviation is classified based onthe abnormal operation that is associated with the matching libraryvector. If the process vector does not match any of the library vectors,then the deviation is classified as unknown. In response toclassification of the deviation, classification system 203 transfersdeviation classification 215 to control system 205.

[0042] Control system 205 receives and processes deviationclassification 215 to provide control instructions 216 to the processcontrol systems and to provide alarms 217 to process operations. Theselection of control instructions and alarms could be based on a look-uptable that associates specific control instructions and alarms withspecific abnormal operations. Control instructions 216 and alarms 217are used to initiate remedial action that counters the abnormaloperation.

[0043] For unknown classifications, deviation classification 215 andalarms 217 indicate the process vector with the unknown classificationand its associated sensor data 212. Classification system 203 typicallyassociates a unique reference number with each process vector and itsclassification, and process vectors with unknown classifications may bedistinguished by their reference number. Personnel at process operationsinvestigate the deviation to determine the associated abnormaloperation. Process operations provide vector information 218 to controlsystem 205. Vector information 218 specifies a new library vector—whichwas the process unit vector that previously had an unknownclassification. Vector information 218 also specifies the abnormaloperation to associate with the new library vector as determined byprocess operations. Vector information 218 also indicates the controlsignals and alarms that should be implemented upon subsequentclassification of the abnormal operation based on the new libraryvector. Control system 205 provides library update 219 to library system204. Library update 219 specifies the new library vector and itsassociated abnormal operation. Library system 204 stores the new libraryvector and its associated abnormal operation in unit vector library 214.Thus, future occurrences of the abnormal operation will be automaticallyclassified, so the proper remedial action can be promptly implemented.Vector library 214 could be distributed and shared among differentprocessing systems at various locations, so that an abnormal operationdiscovered at one location may subsequently be automatically classifiedand countered at a different location.

[0044] If desired, the vector library may be separated into vectorgroups. For example, one vector group could be contamination and asecond vector group could be equipment malfunction. The sequence of thevector comparisons could be established based on the groups, so thathigh impact vector groups are processed before lower impact vectorgroups. Thus, the library vectors for dangerous contaminants could bechecked before the library vectors for minor equipment malfunctions arechecked.

EXAMPLE #3

[0045]FIG. 3 illustrates signal processing and trigger detection system300 in an example of the invention. Signal processing and triggerdetection system 300 is an example of signal processing system 201 andtrigger detection system 202 of FIG. 2. Signal processing and triggerdetection system 300 includes delay time adjustment 301, response timeadjustment 302, baseline estimation 303, baseline estimate subtraction304, scaler 305, principal component analysis 306, distance measurement307, comparator 308, and trigger level 309. Delay time adjustment 301receives various sensor signals from a number of sensors for theprocess. Delay time adjustment 301 time shifts the sensor signals toalign the signals in time. This typically involves delaying the signalsthat arrive early to match the slowest signal in the time domain. Delaytime adjustment 301 transfers time shifted signals to response timeadjustment 302.

[0046] Response time adjustment 302 shifts the slopes of the timeshifted signals to align their response times. For example, responsetime adjustment 302 could use a linear filter to alter the responsetimes of faster signals to match the response time of the slowestsignal. Response time adjustment 302 transfers time and responseadjusted signals to baseline estimation 303 and to baseline estimatesubtraction 304.

[0047] Baseline estimation 303 processes the time and response adjustedsignals to determine baseline values for the sensor signals. Baselineestimation could utilize a moving window average, a Kalman filter thattakes into account both process noise statistics and measurement noisestatistics, or some other baseline determination technique. Baselineestimation 303 transfers baseline estimates to baseline estimatesubtraction 304.

[0048] Baseline estimate subtraction 304 receives both the time andresponse adjusted signals and the baseline estimates. Baseline estimatesubtraction 304 subtracts the baseline estimates from their respectivetime and response adjusted signals to determine a difference from thebaseline estimate for each of the sensor signals. Baseline estimatesubtraction 304 transfers these differences from the baseline estimatesto scaler 305 and to the classification system.

[0049] Scaler 305 adjusts the differences based on weighting factorsthat are specific to the type of difference. Typically, scaler 305multiplies each difference by a specific weighting factor to normalizethe differences, so that one difference does not vary disproportionatelyto the other differences. Scaler 305 transfers the scaled differences toprincipal component analysis 306.

[0050] Principal component analysis 306 processes the scaled differencesusing principal component analysis techniques to reduce the data set.For example, if there are five sensor signals, then there are fivescaled differences. Some of the scaled differences may share redundantinformation. Principal component analysis 306 combines sets of scaleddifferences that share redundant information to reduce the data set. Inan example with five sensor signals, it may be possible to combine a twoof the scaled differences into one difference to reduce the data setfrom five to four differences. Other techniques to reduce the data setand remove redundancy could be used, or principal component analysis 306could be omitted altogether. Principal component analysis 306 transfersthe resulting scaled differences to distance measurement 307.

[0051] Distance measurement 307 processes the resulting scaleddifferences to derive one logical variable that can be used to initiatethe classification process if the one logical variable exceeds athreshold. The logical variable could be derived by squaring each of theresulting scaled differences and then summing the squares. Distancemeasurement 307 transfers the logical variable to comparator 308.

[0052] Trigger level 309 provides a trigger level to comparator 308. Thetrigger level could be set by process operations, determined throughempirical testing, determined based on historical process data, orderived through another suitable technique. To determine the triggerlevel based on historical process data, a Ljung-Box lack-of-fit analysiscould be performed to determine a trigger level that indicates whencurrent differences deviate from historical differences.

[0053] Comparator 308 receives the logical variable and the triggerlevel. Comparator 308 compares the logical variable to the trigger levelto determine if the logical variable exceeds the threshold specified bythe trigger level. If the logical variable exceeds the trigger level,then a deviation is detected, and comparator 308 generates a triggersignal and transfers the trigger signal to the classification system toinitiate classification of the deviation.

[0054]FIG. 4 illustrates classification system 400 in an example of theinvention. Classification system 400 is an example of classificationsystem 203 FIG. 2. Classification system 400 includes classificationcontrol 401, scalers 402-403, unit vectors 404-405, dot product 406,comparator 407, and trigger level 408.

[0055] Classification control 401 receives the differences from thebaseline estimates and the trigger signal from the signal processing andtrigger detection systems. In response to the trigger signal,classification control 401 logs the date, time, reference number, anddifferences for the trigger. In addition, classification control 401retrieves a vector library from the library system. Classificationcontrol 401 transfers the differences to scaler 402 and transferslibrary vectors from the vector library to scaler 403.

[0056] Scaler 402 adjusts the differences based on a weighting factorspecific to each type of difference. Scaler 402 transfers the scaleddifferences to unit vector 404. Scaler 403 adjusts the library vectorsbased on weighting factors that are specific to each component of thelibrary vectors. Scaler 403 transfers the scaled library vectors to unitvector 405. Typically, scalers 402 and 403 apply the same weightingfactors to the same vector components.

[0057] Unit vector 404 processes the scaled differences to generate aprocess unit vector. The process unit vector has a magnitude of one andan angle. Unit vector 404 transfers the process unit vector to dotproduct 406.

[0058] Unit vector 405 processes the scaled library vectors to generatelibrary unit vectors. The library unit vectors also have a magnitude ofone and their respective angles. Unit vector 404 transfers the libraryunit vectors to dot product 406.

[0059] One technique of obtaining a process unit vector is to divideeach difference by the square root of the sum of the squares of alldifferences to produce a vector whose geometrical length is unity. Theinformation about the magnitude of the process vector is lost by design,but the information about the angle of the process vector is retained.Advantageously, the use of a unit vector tends to work well regardlessof the magnitude of the deviation.

[0060] Dot product 406 receives the process unit vector and the libraryunit vectors. To get the angle between the process unit vector and anindividual library unit vector, dot product 406 calculates thearc-cosine of the dot product of the process unit vector and theindividual library unit vector. Dot product 406 repeats the calculationfor all of the library unit vectors and transfers the resulting anglesto comparator 407.

[0061] If desired, the unit vector and dot product steps described abovecould be combined into a single processing step to produce the anglesfrom the scaled differences and scaled library vectors. Alternatively,the use of unit vectors could be omitted, and the process vector and thelibrary vectors could be compared through some other technique todetermine a match.

[0062] Trigger level 408 stores a trigger level that indicates how closean angle must be between the process unit vector and a given libraryunit vector to find a match. The trigger level can be set throughempirical testing or by process operations. Trigger level 408 providesthe trigger level to comparator 407.

[0063] Comparator 407 receives the angles and the trigger level.Comparator 407 compares the angles to the trigger level to determine ifany of the angles fall below the trigger level. If one of the anglesfalls below the trigger level, then comparator 407 generates a matchsignal and transfers the match signal to classification control 401.

[0064] In response to the match signal, classification control 401classifies the deviation by determining the specific abnormal operationthat is associated with the library vector that produced the match.Classification control 401 transfers the classified deviation to thecontrol system. If all of the library vectors are compared without amatch, then classification control 401 transfers an unknownclassification to the control system, along with the reference number,deviations, and other desired data that is associated with the unknownclassification.

EXAMPLE #5

[0065]FIG. 5 illustrates water supply analysis system 500 in an exampleof the invention. Water supply analysis system 500 includes water supply501, water main 502, valve 503, water main 504, sample line 505, sensors506, signal processor 507, computer 508, water supply operations 509,chlorine supply 510, and color supply 511. Water supply 501 providesfresh potable water to water main 502. Under normal operatingconditions, valve 503 is open and allows the water to flow from watermain 502 to water main 504 for distribution to water users.

[0066] Signal processor 507 could be a digital signal processor withfirmware. Computer 508 could be a personal computer with software.Signal processor 507 and computer 508 are an example of processingsystem 105 of FIG. 1. Signal processor 507 is an example of system 201,and computer 508 is an example of systems 202-204 of FIG. 2. Signalprocessor 507 is an example of elements 301-304 of FIG. 3. Computer 508is an example of elements 305-309 of FIG. 3 and elements 401-408 of FIG.4.

[0067] Sample line 505 diverts a portion of the water to sensors 506.Sensors 506 measure pH, conductivity, turbidity, chlorine, and totalorganic carbon in the water from sample line 505. Other measurementscould include temperature, pressure, flow rate, dissolved oxygen,oxidation reduction potential, chemical oxygen demand, resistivity,viscosity, ammonia concentration, fluoride, streaming current potential,ultra-violet light absorbance, fluorescence, and sulfide or other ionsas measured by ion selective electrodes. Sensors 506 could be separatedevices or integrated into a single device. Sensors 506 transfer sensorsignals 512 to signal processor 507.

[0068] Signal processor 507 processes sensor signals 512 to producedifferences 513. Computer 508 processes differences 513 to trigger whena deviation from baseline is detected. In response to the trigger,computer 508 processes the differences to create a process vector andthen compares the process vector to a library of vectors to determine ifthere is a match between the process vector and one of the libraryvectors. If a match is found, computer 508 classifies the deviation byidentifying the operating condition that is associated with the matchinglibrary vector.

[0069] Based on the classified deviation, computer 508 produces alarmsand control signals. Computer 508 transfers alarm 514 indicating theclassified deviation to water supply operations 509. In response tounknown classifications, computer 508 may update the vector library asdescribed above.

[0070] If the classified deviation is a contaminant in the water, thencomputer 508 may transfer control signal 515 to chlorine supply 510, andin response, chlorine supply 510 adds chlorine 518 to water main 502 tocounter the contaminant. In addition, computer 508 may transfer controlsignal 516 to color supply 511., and in response, color supply 511 addscolorant 519 to water main 502 to mark the contaminant. For somecontaminants, computer 508 may transfer control signal 517 to valve 503,and in response, valve 503 operates to stop the flow of contaminatedwater to water main 504.

1. A method of operating a process analysis system to analyze a process,the method comprising: in a plurality of sensors, monitoring the processto generate sensor signals; in a processing system, processing thesensor signals to detect a deviation from a baseline for the process; inthe processing system, generating a process vector for the deviation inresponse to detecting the deviation; and in the processing system,comparing the process vector to a plurality of library vectors toclassify the deviation.
 2. The method of claim 1 wherein the processcomprises a system that supplies water.
 3. The method of claim 2 whereinthe sensor signals indicate pH, conductivity, turbidity, chlorine, andtotal organic carbon of the water.
 4. The method of claim 2 wherein theclassified deviation comprises a contaminant in the water.
 5. The methodof claim 2 further comprising signaling a control system to operate avalve in response to classifying the deviation as a contaminant in thewater.
 6. The method of claim 2 further comprising signaling a controlsystem to add a marker to the water in response to classifying thedeviation as a contaminant in the water.
 7. The method of claim 6wherein the marker comprises a colorant.
 8. The method of claim 2further comprising signaling a control system to perform a treatment onthe water in response to classifying the deviation as a contaminant inthe water.
 9. The method of claim 8 wherein the treatment comprisesadding a disinfectant to the water.
 10. The method of claim 8 whereinthe treatment comprises adding chlorine to the water.
 11. The method ofclaim 8 wherein the treatment comprises exposing the water toultraviolet radiation.
 12. The method of claim 1 wherein processing thesensor signals to detect the deviation from the baseline comprisesprocessing the sensor signals to produce a single variable and comparingthe single variable to a threshold.
 13. The method of claim 1 whereingenerating the process vector for the deviation comprises generating aunit vector.
 14. The method of claim 1 wherein comparing the processvector to the library vectors comprises comparing an angle between theprocess vector and one of the library vectors to a threshold.
 15. Themethod of claim 1 wherein the library vectors are associated withabnormal operations and classifying the deviation comprises identifyingone of the abnormal operations that is associated with one of thelibrary vectors that matches the process vector.
 16. The method of claim1 further comprising, in response to an unknown classification, storingthe process vector as a new one of the library vectors and associatingan abnormal operation with the new library vector.
 17. A processanalysis system comprising: a plurality of sensors configured to monitora process to generate sensor signals; and a processing system configuredto process the sensor signals to detect a deviation from a baseline forthe process, generate a process vector for the deviation in response todetecting the deviation, and compare the process vector to a pluralityof library vectors to classify the deviation.
 18. The process analysissystem of claim 17 wherein the process comprises a system that supplieswater.
 19. The process analysis system of claim 18 wherein the sensorsignals indicate pH, conductivity, turbidity, chlorine, and totalorganic carbon of the water.
 20. The process analysis system of claim 18wherein the classified deviation comprises a contaminant in the water.21. The process analysis system of claim 18 wherein the processingsystem is configured to signal a control system to operate a valve inresponse to classifying the deviation as a contaminant in the water. 22.The process analysis system of claim 18 wherein the processing system isconfigured to signal a control system to add a marker to the water inresponse to classifying the deviation as a contaminant in the water. 23.The process analysis system of claim 22 wherein the marker comprises acolorant.
 24. The process analysis system of claim 18 wherein theprocessing system is configured to signal a control system to perform atreatment on the water in response to classifying the deviation as acontaminant in the water.
 25. The process analysis system of claim 24wherein the treatment comprises adding a disinfectant to the water. 26.The process analysis system of claim 24 wherein the treatment comprisesadding chlorine to the water.
 27. The process analysis system of claim24 wherein the treatment comprises exposing the water to ultravioletradiation.
 28. The process analysis system of claim 17 wherein theprocessing system is configured to process the sensor signals to producea single variable and compare the single variable to a threshold todetect the deviation from the baseline.
 29. The process analysis systemof claim 17 wherein the process vector comprises a unit vector.
 30. Theprocess analysis system of claim 17 wherein the processing system isconfigured to compare an angle between the process vector and one of thelibrary vectors to a threshold.
 31. The process analysis system of claim17 wherein the library vectors are associated with abnormal operationsand the processing system is configured to identify one of the abnormaloperations that is associated with one of the library vectors thatmatches the process vector to classify the deviation.
 32. The processanalysis system of claim 17 wherein the processing system is configuredto store the process vector as a new one of the library vectors andassociate an abnormal operation with the new library vector in responseto an unknown classification.